Advanced configuration examples
In apexcharter: Create Interactive Chart with the JavaScript 'ApexCharts' Library

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(apexcharter)
library(dplyr)

Here some advanced configuration examples to use the full potential of ApexCharts.

Bar chart

This example is taken from {hrbrthemes} readme, it use mpg dataset from {ggplot2}.

Transform data and initialize the chart :

data("mpg", package = "ggplot2")

bars <- count(mpg, class) %>% 
  mutate(pct = n/sum(n)) %>% 
  apex(mapping = aes(class, pct), type = "column")

Change color used :

bars <- bars %>% 
  ax_colors("#617a89")

Modify the y-axis and add a percent symbol after the labels :

bars <- bars %>% 
  ax_yaxis(
    title = list(text = "Weight (tons)"),
    labels = list(
      formatter = format_num(".0%")
    ), 
    tickAmount = 6,
    max = 0.3
  )

Add a title to the x-axis :

bars <- bars %>% 
  ax_xaxis(
    title = list(text = "Fuel efficiency (mpg)")
  )

Modify the tooltip to display "Percentage" instead of the variable name "pct" :

bars <- bars %>% 
  ax_tooltip(
    y = list(
      title = list(
        formatter = JS("function() {return 'Percentage';}")
      )
    )
  )

Add title and subtitle and format them :

bars <- bars %>% 
  ax_labs(
    title = "Seminal ggplot2 column chart example with percents",
    subtitle = "Example taken from {hrbrthemes} readme"
  )  %>% 
  ax_title(
    style = list(fontSize = "22px")
  ) %>% 
  ax_subtitle(
    style = list(fontSize = "16px", color = "#BDBDBD")
  )

Final result looks like :

bars

View full code

data("mpg", package = "ggplot2")

count(mpg, class) %>% 
  mutate(pct = n/sum(n)) %>% 
  apex(mapping = aes(class, pct), type = "column") %>% 
  ax_colors("#617a89")%>% 
  ax_yaxis(
    title = list(text = "Weight (tons)"),
    labels = list(
      formatter = format_num(".0%")
    ), 
    tickAmount = 6,
    max = 0.3
  ) %>% 
  ax_xaxis(
    title = list(text = "Fuel efficiency (mpg)")
  ) %>% 
  ax_tooltip(
    y = list(
      title = list(
        formatter = JS("function() {return 'Percentage';}")
      )
    )
  ) %>% 
  ax_labs(
    title = "Seminal ggplot2 column chart example with percents",
    subtitle = "Example taken from {hrbrthemes} readme"
  )  %>% 
  ax_title(
    style = list(fontSize = "22px")
  ) %>% 
  ax_subtitle(
    style = list(fontSize = "16px", color = "#BDBDBD")
  )

Lines

Dataset used is from the UNHCR (The UN Refugee Agency) and contains data about UNHCR's populations of concern summarised by continent of origin.

Transform data and initialize the chart :

data("unhcr_ts")

lines <- unhcr_ts %>% 
  filter(population_type == "Refugees (incl. refugee-like situations)") %>% 
  mutate(date = as.Date(paste0(year, "-01-01"))) %>% 
  apex(aes(date, n, group = continent_origin), type = "line")

Put the legend at the bottom :

lines <- lines %>% 
  ax_legend(position = "bottom")

Change the width of the lines :

lines <- lines %>% 
  ax_stroke(width = 2)

Change the colors (Viridis palette) :

lines <- lines %>% 
  ax_colors("#440154", "#414487", "#2A788E",
            "#22A884", "#7AD151", "#FDE725")

Data are in million, in the y-axis we divide by 1e6 to limit the number of digits :

lines <- lines %>% 
  ax_yaxis(
    labels = list(
      formatter = JS("function(val) {return (val/1e6).toFixed(0);}")
    ),
    title = list(text = "Number of refugees (in million)")
  )

Only display the years in the x-axis labels :

lines <- lines %>% 
  ax_xaxis(labels = list(format = "yyyy"))

Same in tooltip, and a thousand separator in the value displayed :

lines <- lines %>% 
  ax_tooltip(
    x = list(format = "yyyy"),
    y = list(
      formatter = JS(
        # thousand separator in javascript
        "function(value) {return value.toString().replace(/\\B(?=(\\d{3})+(?!\\d))/g, \",\");}"
      )
    )
  )

Add an annotation to the chart to identify the Great Lakes refugee crisis in 1994 :

lines <- lines %>% 
  ax_annotations(
    points = list(
      list(
        x = JS("new Date('1994').getTime()"),
        y = 6935296,
        label = list(text = "Great Lakes refugee crisis", offsetY = 0),
        marker = list(size = 6)
      )
    )
  )

Add title and subtitle and format them :

lines <- lines %>% 
  ax_labs(
    title = "Continent of origin for refugees population",
    subtitle = "Data from the UN Refugee Agency"
  ) %>% 
  ax_title(
    style = list(fontSize = "22px")
  ) %>% 
  ax_subtitle(
    style = list(fontSize = "16px", color = "#BDBDBD")
  )

Final result looks like :

lines

View full code

data("unhcr_ts")

unhcr_ts %>% 
  filter(population_type == "Refugees (incl. refugee-like situations)") %>% 
  mutate(date = as.Date(paste0(year, "-01-01"))) %>% 
  apex(aes(date, n, group = continent_origin), type = "line") %>% 
  ax_legend(position = "bottom") %>% 
  ax_stroke(width = 2) %>% 
  ax_colors("#440154", "#414487", "#2A788E",
            "#22A884", "#7AD151", "#FDE725") %>%
  ax_yaxis(
    labels = list(
      formatter = htmlwidgets::JS("function(val) {return (val/1e6).toFixed(0);}")
    ),
    title = list(text = "Number of refugees (in million)")
  ) %>% 
  ax_xaxis(labels = list(format = "yyyy")) %>% 
  ax_tooltip(
    x = list(format = "yyyy"),
    y = list(
      formatter = JS(
        # thousand separator in javascript
        "function(value) {return value.toString().replace(/\\B(?=(\\d{3})+(?!\\d))/g, \",\");}"
      )
    )
  ) %>% 
  ax_annotations(
    points = list(
      list(
        x = JS("new Date('1994').getTime()"),
        y = 6935296,
        label = list(text = "Great Lakes refugee crisis", offsetY = 0),
        marker = list(size = 6)
      )
    )
  ) %>% 
  ax_labs(
    title = "Continent of origin for refugees population",
    subtitle = "Data from the UN Refugee Agency"
  ) %>% 
  ax_title(
    style = list(fontSize = "22px")
  ) %>% 
  ax_subtitle(
    style = list(fontSize = "16px", color = "#BDBDBD")
  )

Scatter plot

Dataset used is from {gapminder}.

Transform data and initialize the chart :

data("gapminder", package = "gapminder")

scatter <- gapminder %>% 
  filter(year == 2007) %>% 
  mutate(
    gdpPercap = log(gdpPercap),
    pop = sqrt(pop / pi) / 1500
  ) %>% 
  apex(
    mapping = aes(gdpPercap, 
                  lifeExp, 
                  z = pop, 
                  group = continent, 
                  label = country), 
    type = "scatter", 
    height = "500px"
  )

Enable zoom on both axis :

scatter <- scatter %>% 
  ax_chart(zoom = list(
    enabled = TRUE, type = "xy"
  ))

Show y-axis border and ticks, no decimals in labels :

scatter <- scatter %>% 
  ax_yaxis(
    decimalsInFloat = 0, 
    axisBorder = list(show = TRUE),
    axisTicks = list(show = TRUE),
    title = list(text = "life expectancy at birth (in years)")
  )

Configuration for x-axis, hide the tooltip displayed on the axis itself (not the main tooltip), display two decimals in the labels (useful when zooming, since values are logarithm there's a lot of decimals) :

scatter <- scatter %>% 
  ax_xaxis(
    tickAmount = 8,
    labels = list(
      formatter = JS("function(val) {return val.toFixed(2);}")
    ), 
    tooltip = list(enabled = FALSE),
    title = list(text = "GDP per capita (log-scale)")
  )

Display vertical grid lines (on the x-axis, those on the y-axis are enabled by default) :

scatter <- scatter %>% 
  ax_grid(xaxis = list(lines = list(show = TRUE)))

Legend on the right and slightly offset downwards :

scatter <- scatter %>% 
  ax_legend(position = "right", offsetY = 70)

Add a custom tooltip with an HTML string, data used in aesthetic can be accessed in JavaScript with w.config.series[seriesIndex].data[dataPointIndex].x (x is the variable gdpPercap), or w.config.series[seriesIndex].data[dataPointIndex].label (the country), it's possible to use custom aesthetics to include more data in the chart configuration script.

scatter <- scatter %>% 
  ax_tooltip(custom = JS(paste(
    "function({ series, seriesIndex, dataPointIndex, w }) {",
    "console.log(w); return (",
    "'<div>' +",

    "'<div class = \"apexcharts-tooltip-title\">' +",
    "w.config.series[seriesIndex].data[dataPointIndex].label",
    "+ '</div>' +",
    "'<div style = \"padding: 5px;\">' +",
    "'<div class = \"apexcharts-tooltip-y-group\">' +",

    "'<span class = \"apexcharts-tooltip-text-label\">' +",
    "'Population: ' +", 
    "'</span>' +",
    "'<span class = \"apexcharts-tooltip-text-value\">' +",
    "Math.round(Math.pow(w.config.series[seriesIndex].data[dataPointIndex].z * 1500, 2) * Math.PI).
    toString().replace(/\\B(?=(\\d{3})+(?!\\d))/g, \",\") +",
    "'</span>' +",

    "'</br>' +",

    "'<span class = \"apexcharts-tooltip-text-label\">' +",
    "'GDP per capita: ' +", 
    "'</span>' +",
    "'<span class = \"apexcharts-tooltip-text-value\">' +",
    "Math.round(Math.exp(w.config.series[seriesIndex].data[dataPointIndex].x)) +",
    "'</span>' +",

    "'</br>' +",

    "'<span class = \"apexcharts-tooltip-text-label\">' +",
    "'Life expectancy: ' +", 
    "'</span>' +",
    "'<span class = \"apexcharts-tooltip-text-value\">' +",
    "w.config.series[seriesIndex].data[dataPointIndex].y +",
    "'</span>' +",


    "'</div>' +",
    "'</div>' +",

    "'</div>'",
    ");",
    "}", sep = "\n"
  )))

Add title and subtitle and format them :

scatter <- scatter %>% 
  ax_labs(
    title = "Life expectancy, GDP and population",
    subtitle = "gapminder dataset from {gapminder}"
  ) %>% 
  ax_title(
    style = list(fontSize = "22px")
  ) %>% 
  ax_subtitle(
    style = list(fontSize = "16px", color = "#BDBDBD")
  )

Final result looks like :

scatter

View full code

data("gapminder", package = "gapminder")

gapminder %>% 
  filter(year == 2007) %>% 
  mutate(
    gdpPercap = log(gdpPercap),
    pop = sqrt(pop / pi) / 1500
  ) %>% 
  apex(mapping = aes(gdpPercap, lifeExp, z = pop, group = continent, label = country), type = "scatter") %>% 
  ax_chart(zoom = list(
    enabled = TRUE, type = "xy"
  )) %>% 
  ax_yaxis(
    decimalsInFloat = 0, 
    axisBorder = list(show = TRUE),
    axisTicks = list(show = TRUE),
    title = list(text = "life expectancy at birth (in years)")
  ) %>% 
  ax_xaxis(
    tickAmount = 8, 
    labels = list(
      formatter = JS("function(val) {return val.toFixed(2);}")
    ),
    tooltip = list(enabled = FALSE),
    title = list(text = "GDP per capita (log-scale)")
  ) %>% 
  ax_grid(xaxis = list(lines = list(show = TRUE))) %>% 
  ax_legend(position = "right", offsetY = 70) %>% 
  ax_tooltip(custom = JS(paste(
    "function({ series, seriesIndex, dataPointIndex, w }) {",
    "console.log(w); return (",
    "'<div>' +",

    "'<div class = \"apexcharts-tooltip-title\">' +",
    "w.config.series[seriesIndex].data[dataPointIndex].label",
    "+ '</div>' +",
    "'<div style = \"padding: 5px;\">' +",
    "'<div class = \"apexcharts-tooltip-y-group\">' +",

    "'<span class = \"apexcharts-tooltip-text-label\">' +",
    "'Population: ' +", 
    "'</span>' +",
    "'<span class = \"apexcharts-tooltip-text-value\">' +",
    "Math.round(Math.pow(w.config.series[seriesIndex].data[dataPointIndex].z * 1500, 2) * Math.PI).
    toString().replace(/\\B(?=(\\d{3})+(?!\\d))/g, \",\") +",
    "'</span>' +",

    "'</br>' +",

    "'<span class = \"apexcharts-tooltip-text-label\">' +",
    "'GDP per capita: ' +", 
    "'</span>' +",
    "'<span class = \"apexcharts-tooltip-text-value\">' +",
    "Math.round(Math.exp(w.config.series[seriesIndex].data[dataPointIndex].x)) +",
    "'</span>' +",

    "'</br>' +",

    "'<span class = \"apexcharts-tooltip-text-label\">' +",
    "'Life expectancy: ' +", 
    "'</span>' +",
    "'<span class = \"apexcharts-tooltip-text-value\">' +",
    "w.config.series[seriesIndex].data[dataPointIndex].y +",
    "'</span>' +",


    "'</div>' +",
    "'</div>' +",

    "'</div>'",
    ");",
    "}", sep = "\n"
  ))) %>% 
  ax_labs(
    title = "Life expectancy, GDP and population",
    subtitle = "gapminder dataset from {gapminder}"
  ) %>% 
  ax_title(
    style = list(fontSize = "22px")
  ) %>% 
  ax_subtitle(
    style = list(fontSize = "16px", color = "#BDBDBD")
  )

Heatmap

This is an adaption of this fancy {highcharter} example, based from this WSJ visualization.

data("vaccines", package = "highcharter")

heatmap <- apex(
  vaccines, 
  aes(year, state, fill = count), 
  type = "heatmap", 
  height = "800px"
)

Remove the animations (little slow otherwise) :

heatmap <- heatmap %>% 
  ax_chart(animations = list(enabled = FALSE))

Remove values displayed in the heatmap :

heatmap <- heatmap %>% 
  ax_dataLabels(enabled = FALSE)

Remove space between squared of the heatmap :

heatmap <- heatmap %>% 
  ax_stroke(width = 0)

That's not possible to make a continuous scale in the legend (like with highcharter), so we use breakpoints :

heatmap <- heatmap %>% 
  ax_plotOptions(
    heatmap = heatmap_opts(
      radius = 0,
      enableShades = FALSE,
      colorScale = list(
        ranges = list(
          list(
            from = 0,
            to = 0.001,
            name = "Missing",
            color = "#FFF"
          ),
          list(
            from = 0.001,
            to = 4,
            name = "low",
            # color = "#000004"
            color = "#FDE725"
          ),
          list(
            from = 4,
            to = 70,
            name = "mid-low",
            # color = "#781C6D",
            color = "#35B779"
          ),
          list(
            from = 70,
            to = 290,
            name = "mid-high",
            # color = "#ED6925",
            color = "#31688E"
          ),
          list(
            from = 290,
            to = 3000,
            name = "high",
            # color = "#FCFFA4",
            color = "#440154"
          )
        )
      )
    )
  )

Missing values are colored by default, above we set them to be displayed in white, and now we hide the corresponding legend :

heatmap <- heatmap %>% 
  ax_legend(
    formatter = JS(
      "function(seriesName, opts) {
      if (seriesName == 'Missing') return null; else return seriesName;
      }"
    ),
    offsetY = -15
  )

Set size of the y-axis labels :

heatmap <- heatmap %>% 
  ax_yaxis(
    labels = list(
      style = list(fontSize = "8px")
    )
  )

Add a vertical line to identify the year when the vaccine was introduced :

heatmap <- heatmap %>% 
  ax_annotations(
    xaxis = list(
      list(
        x = 1963, x2 = 1963.1,
        strokeDashArray = 0, 
        opacity = 1, 
        borderColor = "firebrick",
        fillColor = "firebrick",
        label = list(
          borderColor = "firebrick",
          style = list(color = "#FFF", background = "firebrick"),
          text = "Vaccine Introduced", 
          orientation = "horizontal",
          position = "bottom",
          offsetY = 0
        )
      )
    )
  )

As usual, add title and subtitle and format them :

heatmap <- heatmap %>% 
  ax_labs(
    title = "Infectious Diseases and Vaccines",
    subtitle = "vaccines dataset from {highcharter}"
  ) %>% 
  ax_title(
    style = list(fontSize = "22px")
  ) %>% 
  ax_subtitle(
    style = list(fontSize = "16px", color = "#BDBDBD")
  )

Final result looks like :

heatmap

View full code

data("vaccines", package = "highcharter")

apex(vaccines, aes(year, state, fill = count), type = "heatmap") %>% 
  ax_chart(animations = list(enabled = FALSE)) %>% 
  ax_dataLabels(enabled = FALSE) %>% 
  ax_stroke(width = 0) %>% 
  ax_plotOptions(
    heatmap = heatmap_opts(
      radius = 0,
      enableShades = FALSE,
      colorScale = list(
        ranges = list(
          list(
            from = 0,
            to = 0.001,
            name = "Missing",
            color = "#FFF"
          ),
          list(
            from = 0.001,
            to = 4,
            name = "low",
            # color = "#000004"
            color = "#FDE725"
          ),
          list(
            from = 4,
            to = 70,
            name = "mid-low",
            # color = "#781C6D",
            color = "#35B779"
          ),
          list(
            from = 70,
            to = 290,
            name = "mid-high",
            # color = "#ED6925",
            color = "#31688E"
          ),
          list(
            from = 290,
            to = 3000,
            name = "high",
            # color = "#FCFFA4",
            color = "#440154"
          )
        )
      )
    )
  ) %>% 
  ax_legend(
    formatter = JS(
      "function(seriesName, opts) {
      if (seriesName == 'Missing') return null; else return seriesName;
      }"
    ),
    offsetY = -15
  ) %>% 
  ax_yaxis(
    labels = list(
      style = list(fontSize = "8px"),
      offsetY = -20
    )
  ) %>% 
  ax_annotations(
    xaxis = list(
      list(
        x = 1963, x2 = 1963.1,
        strokeDashArray = 0, 
        opacity = 1, 
        borderColor = "firebrick",
        fillColor = "firebrick",
        label = list(
          borderColor = "firebrick",
          style = list(color = "#FFF", background = "firebrick"),
          text = "Vaccine Introduced", 
          orientation = "horizontal",
          position = "bottom",
          offsetY = 0
        )
      )
    )
  ) %>% 
  ax_labs(
    title = "Infectious Diseases and Vaccines",
    subtitle = "vaccines dataset from {highcharter}"
  ) %>% 
  ax_title(
    style = list(fontSize = "22px")
  ) %>% 
  ax_subtitle(
    style = list(fontSize = "16px", color = "#BDBDBD")
  )